With the abundance of spatial and spatio-temporal models in cancer cluster detection, there is an urgent need to make a comparative evaluation among these models to check their relative performances in cluster detection. In this project, we will achieve three specific goals in cancer cluster modeling. The first goal will be achieved by making a comparative evaluation of a variety of spatial models that have been commonly used in cancer mapping within a Bayesian paradigm. A range of spatial models for count data will be considered ranging from random object modeling, data-dependent modeling, random effect modeling, mixture modeling and geostatistical modeling. In this comparative evaluation, we will make a balance judgment between implementation complexity and performance gain. The comparison will be made by a set of cluster detection diagnostics developed in Hossain and Lawson (2006). An extension of these cluster detection diagnostics will also be attempted. The second goal will be achieved by extending cluster detection diagnostics to space-time models. Initially, we propose to extend all the diagnostics proposed in Hossain and Lawson (2006) for spatial models to space-time models. To show application of these space-time cluster detection diagnostics, we will consider a limited number of space-time models in comparison. Finally, the third goal will be achieved by developing flexible software, which will include a variety of cancer cluster diagnostics so that researchers or public health workers assigned with the task of cancer cluster modeling can check the ability of a preferred model in detecting cancer cluster. The software will be developed within the freely available R programming environment. Libraries and GUIs will be developed for use with this package. By using readily accessible software we hope to ensure that the results of our methodological investigations can be made readily available to others. [unreadable] [unreadable] [unreadable]